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<p>This research addresses the local minima problem in the Dynamic Window Approach (DWA) algorithm. The conventional DWA, which does not incorporate prior environmental knowledge, often exhibits degraded goal-reaching performance in complex scenarios, such as environments with U-shaped obstacles, and even when it reaches the goal, the planning time can be relatively long. To overcome this limitation, we propose an improved DWA by integrating Large Language Models (LLMs). Leveraging the reasoning capabilities of LLMs, prior environmental information is interpreted, and appropriate intermediate waypoints are generated. Experimental results in both 2D grid environments and 3D simulation platforms demonstrate that the proposed LLM-based hybrid method achieves higher efficiency and shorter goal-reaching times in U-shaped obstacle scenarios compared to the conventional DWA. These findings highlight the effectiveness of combining the reasoning capabilities of LLMs with DWA to improve navigation performance in complex environments. A video demonstration is available at <a href="https://youtu.be/Otn53HS4KC4">https://youtu.be/Otn53HS4KC4</a>.</p>

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LLM-DWA: a hybrid path planning framework combining large language models with the dynamic window approach

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月19日

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  • Jeonghee Seo,
  • Eunsung Kim,
  • Andrew Jaeyong Choi

摘要

This research addresses the local minima problem in the Dynamic Window Approach (DWA) algorithm. The conventional DWA, which does not incorporate prior environmental knowledge, often exhibits degraded goal-reaching performance in complex scenarios, such as environments with U-shaped obstacles, and even when it reaches the goal, the planning time can be relatively long. To overcome this limitation, we propose an improved DWA by integrating Large Language Models (LLMs). Leveraging the reasoning capabilities of LLMs, prior environmental information is interpreted, and appropriate intermediate waypoints are generated. Experimental results in both 2D grid environments and 3D simulation platforms demonstrate that the proposed LLM-based hybrid method achieves higher efficiency and shorter goal-reaching times in U-shaped obstacle scenarios compared to the conventional DWA. These findings highlight the effectiveness of combining the reasoning capabilities of LLMs with DWA to improve navigation performance in complex environments. A video demonstration is available at https://youtu.be/Otn53HS4KC4.